By Topic

Information Based Model Selection Criterion for Binary Response Generalized Linear Mixed Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Dalei Yu ; Stat. & Math. Coll., Yunnan Univ. of Finance & Econ., Kunming, China ; Yau, K.K.W. ; Chang Ding

Conditional Akaike information criterion is derived within the framework of conditional-likelihood-based method for binary response generalized linear mixed models. The criterion essentially is the asymptotically unbiased estimator of conditional Akaike information based on maximum likelihood estimator. The proposed criterion is adopted to address the model selection problems in binary response generalized linear mixed models. Comparing with other Monte-Carlo EM based methods, conditional Akaike information criterion is more flexible and computationally attractive. Simulations show that the performance of the proposed criterion is in general promising. The use of the criterion is demonstrated in the analysis of the chronic asthmatic patients data.

Published in:

Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on

Date of Conference:

23-26 June 2012